Dr. Xuchao Guo | Smart agriculture | Best Researcher Award

Dr. Xuchao Guo, Shandong Agricultural University , China

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EARLY ACADEMIC PURSUITS🎓

Dr. Guo’s academic journey began with his pursuit of a Ph.D. in Computer Science and Technology at China Agricultural University, from which he graduated in 2022. His early academic endeavors were marked by significant achievements, including his selection for the prestigious China Scholarship Council’s joint doctoral student training program in 2020. This opportunity allowed him to gain invaluable international experience and broaden his research horizons.

PROFESSIONAL ENDEAVORS🏢

Currently, Dr. Guo is employed in the Department of Computer Science at the School of Information Science and Engineering, Shandong Agricultural University. His professional endeavors are focused on teaching, mentoring, and conducting cutting-edge research. As an associate professor and master’s supervisor, he plays a crucial role in shaping the next generation of computer science professionals.

CONTRIBUTIONS AND RESEARCH FOCUS🧪

• Dr. Guo’s research lies at the intersection of natural language processing, multimodal knowledge graph construction, and computer vision. His notable contributions have earned him a position as a reviewer for prestigious journals such as Expert Systems with Applications, Computers and Electronics in Agriculture, and IEEE Transactions on Artificial Intelligence. His work has significantly advanced the field of agricultural intelligence and technology.

 IMPACT AND INFLUENCE🌐

• Dr. Guo has accumulated over 20 publications, with 9 papers as the first or corresponding author. His work includes 6 SCI papers, three of which are in the Chinese Academy of Sciences Zone 1 with a single impact factor of 8.3, and 3 EI papers. His research has made a substantial impact on the scientific community and has been widely recognized for its innovation and applicability.

 ACADEMIC CITATIONS 📰

• 1. Yao, X., Hao, X., Liu, R., Guo, X. (2024). AgCNER, the First Large-Scale Chinese Named Entity Recognition Dataset for Agricultural Diseases and Pests. Sci Data, 11, 769. https://doi.org/10.1038/s41597-024-03578-5. (SCI, Corresponding author)
  2. 王鲁, 刘瑞麟, 黄敬中, 郭旭超. (2024). 基于 CNN-Transformer 的农作物病虫害知识问答意图识别与槽位填充联合模型. 农业工程学报. http://kns.cnki.net/kcms/detail/11.2047.S.20240625.1507.054.html. (EI, Corresponding author)
  3. Hao X, Wang L, Zhu H, Guo X. (2023). Joint agricultural intent detection and slot filling based on enhanced heterogeneous attention mechanism. Computers and Electronics in Agriculture, 207, 107756. (SCI, Corresponding author)
  4. Guo X, Lu S, Tang Z, et al. (2022). CG-ANER: Enhanced contextual embeddings and glyph features-based agricultural named entity recognition. Computers and Electronics in Agriculture, 194, 106776. (SCI)
  5. Guo X, Hao X, Tang Z, et al. (2021). Ace-adp: adversarial contextual embeddings based named entity recognition for agricultural diseases and pests. Agriculture, 11(10), 912. (SCI)